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Books on the topic 'Robotic sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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1

Dahiya, Ravinder S., and Maurizio Valle. Robotic Tactile Sensing. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-0579-1.

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2

NATO Advanced Research Workshop on Highly Redundant Sensing in Robotic Systems (1988 Il Ciocco, Italy). Highly redundant sensing in robotic systems. Berlin: Springer-Verlag, 1990.

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3

Tou, Julius T. Highly Redundant Sensing in Robotic Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990.

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4

Dahiya, Ravinder S. Robotic Tactile Sensing: Technologies and System. Dordrecht: Springer Netherlands, 2013.

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5

Tou, Julius T., and Jens G. Balchen, eds. Highly Redundant Sensing in Robotic Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84051-7.

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6

Janabi-Sharifi, Farrokh, and William Melek, eds. Advances in Motion Sensing and Control for Robotic Applications. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17369-2.

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7

Paley, Derek A., and Norman M. Wereley, eds. Bioinspired Sensing, Actuation, and Control in Underwater Soft Robotic Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-50476-2.

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8

Driels, Morris. Adaptive control of direct drive dexterous robotic hand with bilateral tactile sensing. Monterey, Calif: Naval Postgraduate School, 1990.

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9

universitet, Örebro, IEEE Instrumentation and Measurement Society. TC-15--Virtual Systems in Measurements., IEEE Instrumentation and Measurement Society. TC-27--Human-Computer Interfaces and Interaction., and IEEE Instrumentation and Measurement Society. TC-28--Instrumentation for Robotics & Automation., eds. ROSE' 03: 1st IEEE International Workshop on Robotic Sensing 2003 : Sensing and perception in 21st century robotics : Örebro University, Örebro, Sweden, 5-6 of June, 2003. Piscataway, N.J: IEEE, 2003.

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10

Siores, Elias. Development of a real-time ultrasonic sensing system for automated and robotic welding. Uxbridge: Brunel University, 1988.

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11

Lopez-Juarez, I. On-line Learning for Robotic Assembly Using Artificial Neural Networks and Contact Force Sensing. Nottngham, UK: The Nottingham Trent University, 2000.

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12

C, Gonzalez Rafael, and Lee C. S. G, eds. Robotics: Control, sensing, vision, and intelligence. New York: McGraw-Hill, 1987.

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13

C, Gonzalez Rafael, and Lee C. S. George, eds. Robotics: Control, sensing, vision and intelligence. New York: McGraw-Hill, 1987.

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14

Vinod, B., Richard Voyles, Prahlad Vadakkepat, M. Sundaram, K. S. Sujatha, and J. Joe Brislin, eds. Advancements in Automation, Robotics and Sensing. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2845-8.

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15

Ren, Hongliang, and Jinji Sun, eds. Electromagnetic Actuation and Sensing in Medical Robotics. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6035-9.

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16

Robot tactile sensing. New York: Prentice Hall, 1990.

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17

1961-, Durrant-Whyte Hugh F., ed. Directed sonar sensing for mobile robot navigation. Boston: Kluwer Academic Publishers, 1992.

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18

Najjārīyān, Siyāmik. Artificial tactile sensing in biomedical engineering. New York: McGraw-Hill, 2009.

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19

Najjārīyān, Siyāmik. Artificial Tactile Sensing in Biomedical Engineering. New York: McGraw-Hill, 2009.

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20

Cook, Gerald. Mobile robots: Navigation, control and remote sensing. Hoboken, N.J: Wiley-IEEE Press, 2011.

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21

Horst, Bunke, and Noltemeier Hartmut, eds. Intelligent robots: Sensing, modeling, and planning. Singapore: World Scientific, 1997.

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22

Tactile sensing and displays: Haptic feedback for minimally invasive surgery and robotics. Chichester, West Sussex, U.K: John Wiley & Sons, 2013.

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23

McInroy, John E. Reliable fusion of control and sensing in intelligent machines. Troy, N.Y: Center for Intelligent Robotic Systems for Space Exploration, 1991.

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24

Ōyama, Yasuhiro. Robotto senshingu: Sensa to gazō shingō shori. Tōkyō: Ōmusha, 2007.

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25

Pressure Vessels and Piping Conference (1989 Honolulu, Hawaii). Quality use of the computer: Computational mechanics, artificial intelligence, robotics, and acoustic sensing. New York, N.Y: American Society of Mechanical Engineers, 1989.

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26

Morik, Katharina. Making Robots Smarter: Combining Sensing and Action Through Robot Learning. Boston, MA: Springer US, 1999.

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27

Sensing, intelligence, motion: How robots and humans move in an unstructured world. Hoboken, N.J: Wiley-Interscience, 2006.

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28

Bajcsy, Ruzena. Assembly via disassembly: A case in machine perceptual development. Philadelphia, PA: Dept. of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, 1989.

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29

Highly Redundant Sensing in Robotic Systems. Springer, 2011.

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30

Tou, Julius T. Highly Redundant Sensing in Robotic Systems ). Springer, 2011.

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31

Valle, Maurizio, and Ravinder S. Dahiya. Robotic Tactile Sensing: Technologies and System. Springer, 2012.

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32

Institute Of Electrical and Electronics Engineers. Rose' 03: 1st IEEE International Workshop on Robotic Sensing 2003: Sensing and Perception in 21st Century Robotics: Orebro Unive. Institute of Electrical & Electronics Enginee, 2003.

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33

Ware, Jonathan Andrew. A multi resolution modular sensing system for robotic applications. 1992.

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34

Janabi-Sharifi, Farrokh, and William Melek. Advances in Motion Sensing and Control for Robotic Applications. Springer, 2019.

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35

Paley, Derek A., and Norman M. Wereley. Bioinspired Sensing, Actuation, and Control in Underwater Soft Robotic Systems. Springer, 2020.

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36

Robotic Manipulation on a Moving Platform Utilizing Force Sensing and Sonar Ranging. Storming Media, 1998.

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37

International Workshop on Robotic Sensin. 2004 International Workshop on Robotic Sensing: Robotics in the Automotive Industry, May 24-25, 2004, Graz, Austria: Proceedings. Institute of Electrical & Electronics Enginee, 2003.

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38

Boyer, Frédéric, and Vincent Lebastard. Electric sensing for underwater navigation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0019.

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Underwater navigation in turbid water for exploration in catastrophic conditions or navigation in confined unstructured environments is still a challenge for robotics. In these conditions, neither vision nor sonar can be used. Pursuing a bio-inspired approach in robotics, one can seek solutions in nature to solve this difficult problem. Several hundred fish species in families Gymnotidae and Mormyridae have developed an original sense well adapted to this situation: the electric sense. Gnathonemus petersii first polarizes its body with respect to an electric organ discharge located at the base of its tail and generates a dipolar electric field in its near surroundings. Then, using many transcutaneous electro-receptors distributed along its body, the fish “measures” the distortion of the electric field and infers an image of its surroundings. Understanding and implementing this bio-inspired sense offers the opportunity to enhance the navigation abilities of our underwater robots in confined spaces bathed by turbid waters.
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39

S, Ge S., and Lewis Frank L, eds. Automous mobile robots: Sensing, control, decision-making, and applications. Boca Raton, FL: Taylor & Francis, 2006.

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40

Autonomous mobile robots: Sensing, control, decision-making, and applications. Boca Raton, FL: CRC/Taylor & Francis, 2006.

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41

V, Filippenko Alexei, and Astronomical Society of the Pacific. Summer Scientific Meeting, eds. Robotic telescopes in the 1990s: Proceedings of a symposium held as part of the 103rd annual meeting of the Astronomical Society of the Pacific, at the University of Wyoming, Laramie, Wyoming, 22-24 June 1991. San Francisco, Calif: Astronomical Society of the Pacific, 1992.

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42

R, Nicholls Howard, ed. Advanced tactile sensing for robotics. Singapore: World Scientific Pub., 1992.

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43

Nicholls, Howard R. Advanced Tactile Sensing for Robotics. WORLD SCIENTIFIC, 1992. http://dx.doi.org/10.1142/1573.

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44

Kruusmaa, Maarja. From aquatic animals to robot swimmers. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0044.

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Fish and other aquatic animals have developed a diverse repertoire of locomotion and sensing strategies in an environment that is 800 times denser than air. This chapter explains the underlying principles of aquatic locomotion and describes some landmark biomimetic robots based on those principles. Biological underwater swimmers face the trade-off between speed and manoeuvrability and it is argued that the same trade-off exists also with biomimetic vehicles. Biomimetic underwater vehicles mostly mimic carangiform and subcarangiform swimmers which are fast swimmers. The highly manoeuvrable fish species (lampreys, rays, etc.) are a less popular choice of bioinspiration arguably because of their higher complexity and limitations posed by current technology of electromechanical devices. A unique sensing organ, the lateral line, is utilized by all fish species. Artifical lateral lines for sensing flow are briefly discussed as well as the potential of robot control with the help of flow sensing.
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45

Dudek, Piotr. Vision. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0014.

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Vision is a sensory modality of primary importance to many animal species. The efficient implementation of visual perception is also one of the main challenges in the design of intelligent robotic systems. This chapter reviews the principles of operation and key features of the early stages of biological vision systems. Following the observation that visual information processing starts in the eye, it reviews several approaches to constructing biomimetic artificial vision systems. It presents devices inspired by the morphology of the insects’ compound eyes, and devices tightly integrating image sensing and processing circuitry. These include silicon integrated circuits mimicking the operation of vertebrate retinas, and bio-inspired systems oriented towards machine vision applications, such as dynamic vision sensors and vision chips with pixel-parallel cellular processor arrays. It elucidates the advantages of the near-sensor processing of the visual information, and potential for future developments of neuromorphic vision sensors.
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46

Sun, Jinji, and Hongliang Ren. Electromagnetic Actuation and Sensing in Medical Robotics. Springer, 2018.

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47

Electromagnetic Actuation and Sensing in Medical Robotics. Springer, 2019.

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48

Fu, K. S., R. C. Gonzalez, and C. S. G. Lee. Robotics: Control, Sensing, Vision, and Intelligence (CAD/CAM, robotics, and computer vision). Mcgraw-Hill College, 1987.

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49

Fu, K. S., R. C. Gonzalez, and C. S. G. Lee. Robotics: Control, Sensing, Vision, and Intelligence (CAD/CAM, robotics, and computer vision). Mcgraw-Hill College, 1987.

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50

Fu, K. S. Robotics: Control, Sensing, Vision, and Intelligence (CAD/CAM, Robotics, and Computer Vision). McGraw-Hill Companies, 1987.

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